There are 2 repositories under customer-churn topic.
Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️
Machine Learning, EDA, Classification tasks, Regression tasks for customer churn
Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library
Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer
Analyze your customer database with ease
Utilizing tools such as Spark, Python (PySpark), SQL, and Databricks, performed logistic regression on customers to predict those at a higher risk of churning, then applied the model to an unseen "new customers" data set.
Churn prediction has become a very important part of Syriatel's company strategy. This project uses machine learning algorithms to build a model that can accurately predicts customers who are likely to churn.
Telecom Customer segmentation and Churn Prediction
Visualization and Applying linear models on determining the churn, a hackathon winning project.
We utilize customer account data to visualize churn rate based on various factors. Additionally, we predict customer churn using a logistic regression model provided by scikit-learn.
In this BI consultancy project, I advised the CMO of Maven Communications on how to reduce customer churn, using data.
Derive insights of factors contributing to customer churn in the Telecom Industry.
Prevendo Customer Churn em Operadoras de Telecom
Customer churn prediction with gradient boosted trees
My solution for DataCamp case study "Analyzing Customer Churn in Power BI".
This Python report is designed for a business which is worried by high customer churn.
Prediction of whether or not a customer leaves in an specific period of time, deployed to GCP
Predicting Customer Retention with Machine Learning | Forked my teams original project for further customization
Build a model using XGBoost algorithm to predict customer churn in banking dataset
Using a Telecom's dataset, this project develops both an analysis to understand possible correlations and a model for predicting customer churn.
Analyzing customer data and building machine learning model for predicting customer churn (Logistic Regression, Random Forest and XGBoost). This project is presented as final project for dibimbing's data science bootcamp batch 22 and getting 2nd best final project award in the graduation.
Telecom_customer_churn
This project leverages ML algorithms to predict and tackle customer churn effectively.
predict the churning rate of tele-comunication customer using svm model on Python platform
Customer Churn Prediction in the Banking sector
Creation of a dashboard in Power BI reflecting all relevant Customer Churn's Key Performance Indicators (KPIs) for Phone Now Call Centre.
Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .
PySpark with logistic regression predicting if customers will exit a bank service.
repo to store all assets (such as notebooks, data, etc) for Watson Studio Learning Path tutorials
End-to-End Machine Learning application to predict the customer churn. machine learning is applied to foresee if customers are likely to leave a service. 🤖💼 This involves analyzing customer data, training a model, and predicting churn probabilities. 🚀📊
The repository presented steps for building a model that predicted whether a customer would switch telecommunication service providers.
This project analyzes and predicts customer churn of a music streaming service using Spark on a large dataset.